论文标题
了解机器在多体定位过渡中学到了什么
Learning What a Machine Learns in a Many-Body Localization Transition
论文作者
论文摘要
我们采用卷积神经网络来探索随机旋转系统中不同阶段，旨在了解神经网络选择识别阶段的特定特征。将能源范围归一化为输入数据的能量谱，我们证明了最小的非平凡核宽度的网络将水平间距选择为签名，以将多体局部相和热相区分。我们还研究了核宽度增加的神经网络的性能，基于该核能宽度增加，我们发现了一种替代性诊断，可以从这种无序相互作用系统的原始能量谱中检测阶段。
We employ a convolutional neural network to explore the distinct phases in random spin systems with the aim to understand the specific features that the neural network chooses to identify the phases. With the energy spectrum normalized to the bandwidth as the input data, we demonstrate that a network of the smallest nontrivial kernel width selects level spacing as the signature to distinguish the many-body localized phase from the thermal phase. We also study the performance of the neural network with an increased kernel width, based on which we find an alternative diagnostic to detect phases from the raw energy spectrum of such a disordered interacting system.
